Fast hybrid freehand ultrasound volume reconstruction

Karamalis, Athanasios; Wein, Wolfgang; Kutter, Oliver; Navab, Nassir

doi:10.1117/12.813835

Cited by 20 publications

(20 citation statements)

References 7 publications

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“…The acquired 2D B-Mode US images are sent to the second software component, ImFusion Suite (ImFusion, Munich, Germany), which is a commercial medical imaging software that provides visualization and processing of medical data sets. It reconstructs the received 2D US images to form a 3D US volume based on the wobbler angle information of each 2D US image (Karamalis et al, 2009). ImFusion registers the live 3D US volume to the reference US volume that was recorded on the planning day, using a GPU-optimized mono-modal intensitybased image registration technique with the similarity measure set to Normalized Cross Correlation (NCC), the reference US volume set as a mask, and the optimization algorithm set to BOBYQA (Powell, 2009) for bound-constrained via quadratic models.…”

Section: Human-in-the-loop Image Servoing Strategymentioning

confidence: 99%

Cooperative Control with Ultrasound Guidance for Radiation Therapy

et al. 2016

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Radiation therapy typically begins with the acquisition of a CT scan of the patient for planning, followed by multiple days where radiation is delivered according to the plan. This requires that the patient be reproducibly positioned (set up) on the radiation therapy device (linear accelerator) such that the radiation beams pass through the target. Modern linear accelerators provide cone-beam computed tomography (CBCT) imaging, but this does not provide sufficient contrast to discriminate many abdominal soft-tissue targets, and therefore patient setup is often done by aligning bony anatomy or implanted fiducials. Ultrasound (US) can be used to both assist with patient setup and to provide real-time monitoring of soft-tissue targets. However, one challenge is that the ultrasound probe contact pressure can deform the target area and cause discrepancies with the treatment plan. Another challenge is that radiation therapists typically do not have ultrasound experience and therefore cannot easily find the target in the US image. We propose cooperative control strategies to address both the challenges. First, we use cooperative control with virtual fixtures (VFs) to enable acquisition of a planning CT that includes the soft-tissue deformation. Then, for the patient setup during the treatment sessions, we propose to use real-time US image feedback to dynamically update the VFs; this co-manipulation strategy provides haptic cues that guide the therapist to correctly place the US probe. A phantom study is performed to demonstrate that the co-manipulation strategy enables inexperienced operators to quickly and accurately place the probe on a phantom to reproduce a desired reference image. This is a necessary step for patient setup and, by reproducing the reference image, creates soft-tissue deformations that are consistent with the treatment plan, thereby enabling real-time monitoring during treatment delivery.

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Section: Human-in-the-loop Image Servoing Strategymentioning

confidence: 99%

Cooperative Control with Ultrasound Guidance for Radiation Therapy

et al. 2016

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“…The OpenGL graphics library and shading language (GLSL) are used [9]. In every major iteration of the algorithm, a 3D freehand ultrasound sweep is simulated, followed by a reconstruction into a Cartesian volume [10]. Then, the displacement field update is computed per voxel, smoothed by a low-pass filter, and the deform image is recomputed by warping the source image.…”

Section: Methodsmentioning

confidence: 99%

Automatic non-linear mapping of pre-procedure CT volumes to 3D ultrasound

Wein

Kutter

Aichert

et al. 2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Multi-modality alignment of CT and ultrasound adds value to diagnostic examinations, as well as treatment planning and execution of various clinical procedures. Particularly automatic image-based alignment of such data is challenging, mostly because both modalities have very different imaging physics and characteristics. We present a method for dense-field deformable registration of CT and 3D ultrasound. Compared to global (rigid) alignment, this is more difficult to solve, because modality-specific difference in local anatomic appearance can result in incorrect displacements. We use a simulation of ultrasonic effects based on CT information, taking the current estimate of the deformation field into account to properly address orientation-dependent imaging artifacts. This is combined with a robust multi-channel local similarity metric, driving a variational registration framework. Because of the high computational demand, an efficient GPU-based implementation is used. Preliminary results are shown on data from a number of hepatic cancer patients. To our knowledge, this is the first time that a non-linear mapping of CT and 3D B-mode ultrasound is established in a computationally practical and fully automatic manner.

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“…To this end, individual 2D B-mode frames are compounded using a GPU-based backward warping strategy as in [26]. For every single frame, the full chain of transformations into the world coordinate frame F w is considered (see Fig.…”

Section: D Ultrasound Image Acquisition and Preparationmentioning

confidence: 99%

3D ultrasound registration-based visual servoing for neurosurgical navigation

et al. 2017

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Purpose We present a fully image-based visual servoing framework for neurosurgical navigation and needle guidance. The proposed servo-control scheme allows for compensation of target anatomy movements, maintaining high navigational accuracy over time, and automatic needle guide alignment for accurate manual insertions. Method Our system comprises a motorized 3D ultrasound (US) transducer mounted on a robotic arm and equipped with a needle guide. It continuously registers US sweeps in real time with a pre-interventional plan based on CT or MR images and annotations. While a visual control law maintains anatomy visibility and alignment of the needle guide, a force controller is employed for acoustic coupling and tissue pressure. We validate the servoing capabilities of our method on a geometric gel phantom and real human anatomy, and the needle targeting accuracy using CT images on a lumbar spine gel phantom under neurosurgery conditions. Results Despite the varying resolution of the acquired Oliver Zettinig and Benjamin Frisch have contributed equally to this work. Yu-Mi Ryang and Nassir Navab have contributed equally to this work.B Oliver Zettinig 3D sweeps, we achieved direction-independent positioning errors of 0.35±0.19 mm and 0.61 • ±0.45 • , respectively. Our method is capable of compensating movements of around 25 mm/s and works reliably on human anatomy with errors of 1.45 ± 0.78 mm. In all four manual insertions by an expert surgeon, a needle could be successfully inserted into the facet joint, with an estimated targeting accuracy of 1.33±0.33 mm, superior to the gold standard. Conclusion The experiments demonstrated the feasibility of robotic ultrasound-based navigation and needle guidance for neurosurgical applications such as lumbar spine injections.

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Fast hybrid freehand ultrasound volume reconstruction

Cited by 20 publications

References 7 publications

Cooperative Control with Ultrasound Guidance for Radiation Therapy

Cooperative Control with Ultrasound Guidance for Radiation Therapy

Automatic non-linear mapping of pre-procedure CT volumes to 3D ultrasound

3D ultrasound registration-based visual servoing for neurosurgical navigation

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